Overview

Dataset statistics

Number of variables21
Number of observations200
Missing cells40
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.9 KiB
Average record size in memory168.6 B

Variable types

NUM20
DATE1

Warnings

NCFP_min is highly correlated with n_cashflow_actHigh correlation
n_cashflow_act is highly correlated with NCFP_minHigh correlation
VSTD_6d_max is highly correlated with turnover_vol_5d_maxHigh correlation
turnover_vol_5d_max is highly correlated with VSTD_6d_maxHigh correlation
turnover_vol_5d_min is highly correlated with VSTD_6d_minHigh correlation
VSTD_6d_min is highly correlated with turnover_vol_5d_minHigh correlation
TO_100d_min has 2 (1.0%) missing values Missing
BP_LF_min has 2 (1.0%) missing values Missing
n_cashflow_act has 2 (1.0%) missing values Missing
TO_5d_min has 2 (1.0%) missing values Missing
DAVOL20_min has 2 (1.0%) missing values Missing
turnover_vol_5d_max has 2 (1.0%) missing values Missing
DAVOL5_min has 2 (1.0%) missing values Missing
NCFP_min has 2 (1.0%) missing values Missing
VSTD_6d_min has 2 (1.0%) missing values Missing
opincome_of_ebt has 2 (1.0%) missing values Missing
turnover_vol_100d_min has 2 (1.0%) missing values Missing
ocf_to_or has 2 (1.0%) missing values Missing
current_ratio has 2 (1.0%) missing values Missing
wgt_turn_6m has 2 (1.0%) missing values Missing
VSTD_6d_max has 2 (1.0%) missing values Missing
PPReversal_20_min has 2 (1.0%) missing values Missing
turnover_vol_20d_max has 2 (1.0%) missing values Missing
assets_turn has 2 (1.0%) missing values Missing
TO_20d_min has 2 (1.0%) missing values Missing
turnover_vol_5d_min has 2 (1.0%) missing values Missing
date has unique values Unique

Reproduction

Analysis started2020-12-18 14:49:39.706320
Analysis finished2020-12-18 14:50:49.694745
Duration1 minute and 9.99 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

date
Date

UNIQUE

Distinct200
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
Minimum2013-10-06 00:00:00
Maximum2017-07-30 00:00:00
2020-12-18T22:50:49.796392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:50.282230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TO_100d_min
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.4126536343
Minimum-0.9557015091
Maximum2.497548154
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:50.521490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.9557015091
5-th percentile-0.9097148365
Q1-0.6241039141
median0.2624818494
Q31.220540182
95-th percentile2.14805531
Maximum2.497548154
Range3.453249663
Interquartile range (IQR)1.844644097

Descriptive statistics

Standard deviation1.037002778
Coefficient of variation (CV)2.513010166
Kurtosis-1.121444716
Mean0.4126536343
Median Absolute Deviation (MAD)0.8898857308
Skewness0.3917153171
Sum81.70541958
Variance1.075374761
MonotocityNot monotonic
2020-12-18T22:50:50.727703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.86150696521.0%
 
2.09112783621.0%
 
-0.736337329210.5%
 
0.229771783710.5%
 
0.225995115910.5%
 
-0.00569154704310.5%
 
-0.201949264610.5%
 
-0.867701668210.5%
 
-0.620264346410.5%
 
1.61575857110.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.955701509110.5%
 
-0.952555981110.5%
 
-0.95237397310.5%
 
-0.944900399610.5%
 
-0.941857652610.5%
 
ValueCountFrequency (%) 
2.49754815410.5%
 
2.49731047310.5%
 
2.48886208610.5%
 
2.36985395510.5%
 
2.36951188710.5%
 

BP_LF_min
Real number (ℝ)

MISSING

Distinct192
Distinct (%)97.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.275570381
Minimum-1.784026809
Maximum1.24609317
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:50.960801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.784026809
5-th percentile-1.382361948
Q1-0.8025299879
median-0.3624542368
Q30.2675511385
95-th percentile0.9949649674
Maximum1.24609317
Range3.030119979
Interquartile range (IQR)1.070081126

Descriptive statistics

Standard deviation0.7187607458
Coefficient of variation (CV)-2.608265602
Kurtosis-0.5914787174
Mean-0.275570381
Median Absolute Deviation (MAD)0.4892019542
Skewness0.1435327406
Sum-54.56293545
Variance0.5166170097
MonotocityNot monotonic
2020-12-18T22:50:51.170854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.609177621.0%
 
-0.889517463721.0%
 
0.99664224621.0%
 
1.17321960421.0%
 
-0.481524589721.0%
 
-1.61914983421.0%
 
-0.277457434310.5%
 
-0.757870398610.5%
 
-1.13097201810.5%
 
0.347228162110.5%
 
Other values (182)18291.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-1.78402680910.5%
 
-1.77801604910.5%
 
-1.75337984110.5%
 
-1.74917894710.5%
 
-1.61914983421.0%
 
ValueCountFrequency (%) 
1.2460931710.5%
 
1.21271548410.5%
 
1.19622145310.5%
 
1.17321960421.0%
 
1.06875059810.5%
 

n_cashflow_act
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct26
Distinct (%)13.1%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.0582831862
Minimum-1.656739778
Maximum1.255993047
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:51.325213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.656739778
5-th percentile-1.656739778
Q1-0.643507077
median-0.08713222998
Q30.556155474
95-th percentile1.255993047
Maximum1.255993047
Range2.912732825
Interquartile range (IQR)1.199662551

Descriptive statistics

Standard deviation0.7639137302
Coefficient of variation (CV)-13.10693152
Kurtosis-0.4819269641
Mean-0.0582831862
Median Absolute Deviation (MAD)0.556374847
Skewness-0.0430312213
Sum-11.54007087
Variance0.5835641872
MonotocityNot monotonic
2020-12-18T22:50:51.475100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
-0.9739910697136.5%
 
0.556155474136.5%
 
1.255993047136.5%
 
0.1876779571136.5%
 
-0.643507077136.5%
 
0.0550708916126.0%
 
0.04434183859126.0%
 
-0.7817862558126.0%
 
-0.08713222998126.0%
 
0.7494322841126.0%
 
Other values (16)7336.5%
 
ValueCountFrequency (%) 
-1.656739778126.0%
 
-0.9739910697136.5%
 
-0.7817862558126.0%
 
-0.702390939110.5%
 
-0.643507077136.5%
 
ValueCountFrequency (%) 
1.255993047136.5%
 
1.098600671126.0%
 
1.04181118742.0%
 
0.858919409410.5%
 
0.7494322841126.0%
 

TO_5d_min
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.2673181889
Minimum-0.8861138081
Maximum3.353583178
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:51.644581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8861138081
5-th percentile-0.802381604
Q1-0.5953207797
median-0.001752198321
Q30.8778653574
95-th percentile2.212222057
Maximum3.353583178
Range4.239696986
Interquartile range (IQR)1.473186137

Descriptive statistics

Standard deviation0.99887036
Coefficient of variation (CV)3.736634474
Kurtosis0.2574813332
Mean0.2673181889
Median Absolute Deviation (MAD)0.6607266692
Skewness0.9890574156
Sum52.9290014
Variance0.9977419962
MonotocityNot monotonic
2020-12-18T22:50:51.823725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.03753366521.0%
 
2.21222205721.0%
 
-0.345939729710.5%
 
-0.717976140510.5%
 
-0.28582195810.5%
 
3.35358317810.5%
 
0.159694837810.5%
 
-0.789563566310.5%
 
-0.681552995410.5%
 
1.29193618510.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.886113808110.5%
 
-0.862214033110.5%
 
-0.857421023310.5%
 
-0.852329615910.5%
 
-0.8422773510.5%
 
ValueCountFrequency (%) 
3.35358317810.5%
 
3.19990846410.5%
 
3.15772251710.5%
 
3.12701554910.5%
 
2.41046989710.5%
 

DAVOL20_min
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.08979340963
Minimum-1.155652881
Maximum1.478709322
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:51.992788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.155652881
5-th percentile-0.9996411422
Q1-0.5705066233
median-0.2116931796
Q30.4311852513
95-th percentile1.036845494
Maximum1.478709322
Range2.634362203
Interquartile range (IQR)1.001691875

Descriptive statistics

Standard deviation0.6571178246
Coefficient of variation (CV)-7.318107501
Kurtosis-0.8397602553
Mean-0.08979340963
Median Absolute Deviation (MAD)0.5349778787
Skewness0.3547287662
Sum-17.77909511
Variance0.4318038353
MonotocityNot monotonic
2020-12-18T22:50:52.173660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.199673518221.0%
 
0.7062587821.0%
 
0.273852402410.5%
 
-1.07959273210.5%
 
-0.637611470410.5%
 
-0.494155161910.5%
 
-0.560392600610.5%
 
-0.849217991110.5%
 
-0.485261288210.5%
 
-0.346558667110.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-1.15565288110.5%
 
-1.12178082110.5%
 
-1.11510746710.5%
 
-1.08846652810.5%
 
-1.07959273210.5%
 
ValueCountFrequency (%) 
1.47870932210.5%
 
1.46792122110.5%
 
1.40076390810.5%
 
1.26554052410.5%
 
1.26442710510.5%
 

turnover_vol_5d_max
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct192
Distinct (%)97.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.1062992215
Minimum-0.8939142429
Maximum3.718203507
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:52.351811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8939142429
5-th percentile-0.7789110016
Q1-0.5941211956
median-0.1447678635
Q30.4121666167
95-th percentile2.122027249
Maximum3.718203507
Range4.61211775
Interquartile range (IQR)1.006287812

Descriptive statistics

Standard deviation0.9771348378
Coefficient of variation (CV)9.192304743
Kurtosis4.370357582
Mean0.1062992215
Median Absolute Deviation (MAD)0.475013235
Skewness2.00732323
Sum21.04724585
Variance0.9547924912
MonotocityNot monotonic
2020-12-18T22:50:52.524900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.71820350752.5%
 
1.34847503321.0%
 
1.97895163521.0%
 
-0.224903100210.5%
 
-0.710514208910.5%
 
-0.0837454804710.5%
 
-0.641907456510.5%
 
-0.347796613110.5%
 
0.983214829310.5%
 
-0.808547946810.5%
 
Other values (182)18291.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.893914242910.5%
 
-0.856273007410.5%
 
-0.836488100310.5%
 
-0.825399291510.5%
 
-0.811619425310.5%
 
ValueCountFrequency (%) 
3.71820350752.5%
 
3.63464503310.5%
 
3.17825328910.5%
 
2.38878556110.5%
 
2.36743452710.5%
 

DAVOL5_min
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.08401223169
Minimum-1.001059333
Maximum2.622952506
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:52.712790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.001059333
5-th percentile-0.9088418695
Q1-0.5903304755
median-0.2212133881
Q30.3492463601
95-th percentile1.236884384
Maximum2.622952506
Range3.624011838
Interquartile range (IQR)0.9395768356

Descriptive statistics

Standard deviation0.6721462759
Coefficient of variation (CV)-8.000576373
Kurtosis1.239036014
Mean-0.08401223169
Median Absolute Deviation (MAD)0.4705798996
Skewness1.063742117
Sum-16.63442187
Variance0.4517806162
MonotocityNot monotonic
2020-12-18T22:50:52.915334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.501481050321.0%
 
-0.297544649321.0%
 
-0.700837327610.5%
 
1.57307329110.5%
 
1.09095922610.5%
 
-0.0706784823610.5%
 
2.62295250610.5%
 
-0.714349242610.5%
 
-0.881459828910.5%
 
-0.723546457310.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-1.00105933310.5%
 
-0.987188519510.5%
 
-0.982656902410.5%
 
-0.976243087610.5%
 
-0.971230058310.5%
 
ValueCountFrequency (%) 
2.62295250610.5%
 
2.07163763910.5%
 
1.9318235510.5%
 
1.57746769710.5%
 
1.57307329110.5%
 

NCFP_min
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct191
Distinct (%)96.5%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.1980761927
Minimum-1.652795634
Maximum1.604264254
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:53.127238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.652795634
5-th percentile-1.372736406
Q1-0.6872341195
median-0.327062638
Q30.2217732269
95-th percentile1.251707994
Maximum1.604264254
Range3.257059888
Interquartile range (IQR)0.9090073464

Descriptive statistics

Standard deviation0.6958859429
Coefficient of variation (CV)-3.513223539
Kurtosis0.297740018
Mean-0.1980761927
Median Absolute Deviation (MAD)0.4884854841
Skewness0.4068793787
Sum-39.21908616
Variance0.4842572455
MonotocityNot monotonic
2020-12-18T22:50:53.446285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.5091133621.0%
 
-0.689914995621.0%
 
1.60426425421.0%
 
-0.500250789921.0%
 
-0.381735122521.0%
 
-0.524993260421.0%
 
0.0941799138821.0%
 
-1.561647410.5%
 
-1.06203357810.5%
 
-0.501678010110.5%
 
Other values (181)18190.5%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-1.65279563410.5%
 
-1.57395561210.5%
 
-1.561647410.5%
 
-1.55944502210.5%
 
-1.54220535110.5%
 
ValueCountFrequency (%) 
1.60426425421.0%
 
1.53721428410.5%
 
1.51308366310.5%
 
1.5091133621.0%
 
1.49728907410.5%
 

VSTD_6d_min
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct185
Distinct (%)93.4%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.2929415445
Minimum-0.8088934028
Maximum2.987844599
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:53.762913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8088934028
5-th percentile-0.7203368437
Q1-0.53117216
median-0.1316028595
Q30.7376263328
95-th percentile2.987844599
Maximum2.987844599
Range3.796738001
Interquartile range (IQR)1.268798493

Descriptive statistics

Standard deviation1.122080207
Coefficient of variation (CV)3.830389468
Kurtosis0.4921920124
Mean0.2929415445
Median Absolute Deviation (MAD)0.4736604724
Skewness1.282401738
Sum58.00242581
Variance1.259063991
MonotocityNot monotonic
2020-12-18T22:50:53.985564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.987844599147.0%
 
0.363211036210.5%
 
0.196161343510.5%
 
0.303821228410.5%
 
0.146551704510.5%
 
-0.762005517310.5%
 
1.57810626210.5%
 
0.716892104710.5%
 
-0.662417670410.5%
 
-0.651180301910.5%
 
Other values (175)17587.5%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.808893402810.5%
 
-0.790437478810.5%
 
-0.783987585710.5%
 
-0.764025312810.5%
 
-0.762037656510.5%
 
ValueCountFrequency (%) 
2.987844599147.0%
 
2.90816147210.5%
 
2.77411162610.5%
 
2.67490440610.5%
 
2.45553108910.5%
 

opincome_of_ebt
Real number (ℝ)

MISSING

Distinct22
Distinct (%)11.1%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.4825840176
Minimum-0.7695906706
Maximum3.982696365
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:54.216906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.7695906706
5-th percentile-0.7695906706
Q1-0.4883745934
median-0.08510969522
Q30.6904777086
95-th percentile3.982696365
Maximum3.982696365
Range4.752287036
Interquartile range (IQR)1.178852302

Descriptive statistics

Standard deviation1.489356881
Coefficient of variation (CV)3.08621261
Kurtosis1.385626993
Mean0.4825840176
Median Absolute Deviation (MAD)0.5922053026
Skewness1.632497492
Sum95.55163548
Variance2.218183918
MonotocityNot monotonic
2020-12-18T22:50:54.379841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%) 
3.9826963652713.5%
 
-0.6807064819136.5%
 
-0.4632392219136.5%
 
0.3493219822136.5%
 
0.9084721424136.5%
 
-0.4278996601136.5%
 
0.5070956074136.5%
 
-0.08510969522126.0%
 
-0.4883745934126.0%
 
-0.3518109233126.0%
 
Other values (12)5728.5%
 
ValueCountFrequency (%) 
-0.7695906706115.5%
 
-0.722721324410.5%
 
-0.6807064819136.5%
 
-0.6220739531126.0%
 
-0.596680401742.0%
 
ValueCountFrequency (%) 
3.9826963652713.5%
 
0.9084721424136.5%
 
0.6904777086115.5%
 
0.690477708621.0%
 
0.5725543597115.5%
 

turnover_vol_100d_min
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.130299846
Minimum-0.8433017694
Maximum0.7948672307
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:54.542311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8433017694
5-th percentile-0.8219696378
Q1-0.6930164015
median-0.2170703782
Q30.4727252469
95-th percentile0.6641441608
Maximum0.7948672307
Range1.638169
Interquartile range (IQR)1.165741648

Descriptive statistics

Standard deviation0.5533446086
Coefficient of variation (CV)-4.246701939
Kurtosis-1.499819705
Mean-0.130299846
Median Absolute Deviation (MAD)0.5360287943
Skewness0.2046058442
Sum-25.79936951
Variance0.3061902559
MonotocityNot monotonic
2020-12-18T22:50:54.721154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.277699059621.0%
 
0.435948502521.0%
 
-0.44876289510.5%
 
-0.376749285610.5%
 
-0.0267691095610.5%
 
0.564452440210.5%
 
0.197072865210.5%
 
0.0143533573810.5%
 
-0.787853670410.5%
 
-0.0895424633410.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.843301769410.5%
 
-0.834881113510.5%
 
-0.833600696110.5%
 
-0.83348859110.5%
 
-0.833255266710.5%
 
ValueCountFrequency (%) 
0.794867230710.5%
 
0.774421173110.5%
 
0.772327153410.5%
 
0.736252835310.5%
 
0.732143100710.5%
 

ocf_to_or
Real number (ℝ)

MISSING

Distinct26
Distinct (%)13.1%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.008052033465
Minimum-2.800952281
Maximum0.996412974
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:54.885065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2.800952281
5-th percentile-2.800952281
Q1-0.4904042051
median0.2923110455
Q30.5623307171
95-th percentile0.996412974
Maximum0.996412974
Range3.797365255
Interquartile range (IQR)1.052734922

Descriptive statistics

Standard deviation0.8674598972
Coefficient of variation (CV)-107.7317799
Kurtosis3.978048028
Mean-0.008052033465
Median Absolute Deviation (MAD)0.3417970527
Skewness-1.910637761
Sum-1.594302626
Variance0.7524866732
MonotocityNot monotonic
2020-12-18T22:50:55.064793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
-0.2716540914136.5%
 
0.996412974136.5%
 
-0.4904042051136.5%
 
0.6341080981136.5%
 
0.1350844013136.5%
 
-0.6613027314136.5%
 
0.353834515126.0%
 
0.6648698329126.0%
 
-2.800952281126.0%
 
0.5623307171126.0%
 
Other values (16)7236.0%
 
ValueCountFrequency (%) 
-2.800952281126.0%
 
-0.901927856510.5%
 
-0.6954824367126.0%
 
-0.6613027314136.5%
 
-0.610716767610.5%
 
ValueCountFrequency (%) 
0.996412974136.5%
 
0.924635592942.0%
 
0.736647213910.5%
 
0.6648698329126.0%
 
0.6341080981136.5%
 

current_ratio
Real number (ℝ)

MISSING

Distinct25
Distinct (%)12.6%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.5642259945
Minimum-0.7565471019
Maximum-0.3586473603
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:55.239165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.7565471019
5-th percentile-0.7032478063
Q1-0.6612233617
median-0.5419149384
Q3-0.4945605642
95-th percentile-0.3586473603
Maximum-0.3586473603
Range0.3978997416
Interquartile range (IQR)0.1666627975

Descriptive statistics

Standard deviation0.09844040508
Coefficient of variation (CV)-0.1744698153
Kurtosis-0.6818555016
Mean-0.5642259945
Median Absolute Deviation (MAD)0.06539413579
Skewness0.07981543875
Sum-111.7167469
Variance0.009690513353
MonotocityNot monotonic
2020-12-18T22:50:55.415883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
-0.7032478063136.5%
 
-0.6636833292136.5%
 
-0.5144453014136.5%
 
-0.5132153176136.5%
 
-0.6925879472136.5%
 
-0.6612233617136.5%
 
-0.5525747975126.0%
 
-0.5419149384126.0%
 
-0.5257201524126.0%
 
-0.3586473603126.0%
 
Other values (15)7236.0%
 
ValueCountFrequency (%) 
-0.756547101942.0%
 
-0.7032478063136.5%
 
-0.6925879472136.5%
 
-0.6636833292136.5%
 
-0.6612233617136.5%
 
ValueCountFrequency (%) 
-0.3586473603126.0%
 
-0.38054107110.5%
 
-0.441261268610.5%
 
-0.468115913710.5%
 
-0.4681159137126.0%
 

wgt_turn_6m
Real number (ℝ)

MISSING

Distinct21
Distinct (%)10.6%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.1261816574
Minimum-1.049619946
Maximum1.555745903
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:55.587606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.049619946
5-th percentile-1.049619946
Q1-0.891651998
median-0.4459363399
Q30.5909018578
95-th percentile1.555745903
Maximum1.555745903
Range2.605365849
Interquartile range (IQR)1.482553856

Descriptive statistics

Standard deviation0.858819637
Coefficient of variation (CV)-6.806216169
Kurtosis-0.6503214881
Mean-0.1261816574
Median Absolute Deviation (MAD)0.5586711855
Skewness0.7336059388
Sum-24.98396817
Variance0.737571169
MonotocityNot monotonic
2020-12-18T22:50:55.748935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
0.11273484562613.0%
 
-1.0496199462512.5%
 
0.59090185782412.0%
 
-0.8916519982211.0%
 
-0.44593633992010.0%
 
-0.8587653241199.5%
 
1.555745903189.0%
 
-0.06797861944136.5%
 
1.55574590342.0%
 
1.49222775742.0%
 
Other values (11)2311.5%
 
ValueCountFrequency (%) 
-1.0496199462512.5%
 
-0.92235844342.0%
 
-0.89165199821.0%
 
-0.8916519982211.0%
 
-0.89165199821.0%
 
ValueCountFrequency (%) 
1.555745903189.0%
 
1.55574590342.0%
 
1.49222775742.0%
 
0.799368730342.0%
 
0.59090185782412.0%
 

VSTD_6d_max
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct181
Distinct (%)91.4%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.2926706999
Minimum-0.7979503122
Maximum2.999371288
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:55.911776image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.7979503122
5-th percentile-0.7485743975
Q1-0.5263065983
median-0.100972989
Q30.6063935776
95-th percentile2.999371288
Maximum2.999371288
Range3.7973216
Interquartile range (IQR)1.132700176

Descriptive statistics

Standard deviation1.125205429
Coefficient of variation (CV)3.844612492
Kurtosis0.5880756378
Mean0.2926706999
Median Absolute Deviation (MAD)0.4836810884
Skewness1.308332873
Sum57.94879858
Variance1.266087257
MonotocityNot monotonic
2020-12-18T22:50:56.123228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.999371288178.5%
 
2.15218669221.0%
 
-0.374159160710.5%
 
0.0579469690710.5%
 
-0.0347890173810.5%
 
-0.68796226810.5%
 
-0.705529534610.5%
 
0.183392978610.5%
 
1.48760205110.5%
 
-0.115430033410.5%
 
Other values (171)17185.5%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.797950312210.5%
 
-0.79794533510.5%
 
-0.793507414110.5%
 
-0.774033353710.5%
 
-0.764005603710.5%
 
ValueCountFrequency (%) 
2.999371288178.5%
 
2.45898059610.5%
 
2.36696935910.5%
 
2.15778412610.5%
 
2.15218669221.0%
 

PPReversal_20_min
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.1180671681
Minimum-2.848572044
Maximum2.691846373
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:56.305572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-2.848572044
5-th percentile-1.901782382
Q1-0.4480791811
median0.1816867671
Q30.6720140372
95-th percentile1.899906337
Maximum2.691846373
Range5.540418417
Interquartile range (IQR)1.120093218

Descriptive statistics

Standard deviation1.058291991
Coefficient of variation (CV)8.963474
Kurtosis0.4718476234
Mean0.1180671681
Median Absolute Deviation (MAD)0.5575586721
Skewness-0.3422347995
Sum23.37729928
Variance1.119981939
MonotocityNot monotonic
2020-12-18T22:50:56.493392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.10935885121.0%
 
1.81248634521.0%
 
-0.360808844410.5%
 
1.05808573110.5%
 
0.474459521210.5%
 
-0.706027533510.5%
 
-0.64124109110.5%
 
0.296055336910.5%
 
1.06231268410.5%
 
0.562296617510.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-2.84857204410.5%
 
-2.75121007210.5%
 
-2.58111528510.5%
 
-2.51223177910.5%
 
-2.46959677810.5%
 
ValueCountFrequency (%) 
2.69184637310.5%
 
2.49927844910.5%
 
2.29146708110.5%
 
2.26922968810.5%
 
2.24169480510.5%
 

turnover_vol_20d_max
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.01803920759
Minimum-0.8594448417
Maximum1.852643462
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:56.676720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8594448417
5-th percentile-0.8064563011
Q1-0.582507573
median-0.2450360153
Q30.3940311046
95-th percentile1.579765267
Maximum1.852643462
Range2.712088303
Interquartile range (IQR)0.9765386776

Descriptive statistics

Standard deviation0.718708139
Coefficient of variation (CV)-39.84144732
Kurtosis0.2029796373
Mean-0.01803920759
Median Absolute Deviation (MAD)0.3931783933
Skewness1.060608737
Sum-3.571763104
Variance0.5165413891
MonotocityNot monotonic
2020-12-18T22:50:56.868868image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.611890267521.0%
 
0.63774566821.0%
 
1.16967110910.5%
 
1.70985626310.5%
 
-0.177844497710.5%
 
-0.114277390710.5%
 
-0.33367391110.5%
 
1.52772514110.5%
 
-0.743215858810.5%
 
-0.00661746976210.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.859444841710.5%
 
-0.857890636410.5%
 
-0.853669164410.5%
 
-0.847036880210.5%
 
-0.836320715210.5%
 
ValueCountFrequency (%) 
1.85264346210.5%
 
1.83306953710.5%
 
1.74664483410.5%
 
1.73066832810.5%
 
1.70985626310.5%
 

assets_turn
Real number (ℝ)

MISSING

Distinct26
Distinct (%)13.1%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean-0.4961992795
Minimum-1.054097199
Maximum0.2428309472
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:57.032097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-1.054097199
5-th percentile-1.054097199
Q1-0.8409570894
median-0.684448982
Q3-0.04519978609
95-th percentile0.2428309472
Maximum0.2428309472
Range1.296928146
Interquartile range (IQR)0.7957573033

Descriptive statistics

Standard deviation0.4353962059
Coefficient of variation (CV)-0.8774623905
Kurtosis-1.246147642
Mean-0.4961992795
Median Absolute Deviation (MAD)0.3676736006
Skewness0.2431794362
Sum-98.24745733
Variance0.1895698561
MonotocityNot monotonic
2020-12-18T22:50:57.199960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
-1.052122583136.5%
 
-0.6969548868136.5%
 
-0.3040061945136.5%
 
0.1238273901136.5%
 
-0.007813712853136.5%
 
-1.038300267126.0%
 
-0.7542187666126.0%
 
-0.684448982126.0%
 
-0.6949802703126.0%
 
-0.2858397223126.0%
 
Other values (16)7336.5%
 
ValueCountFrequency (%) 
-1.054097199115.5%
 
-1.052122583136.5%
 
-1.038300267126.0%
 
-1.021450206126.0%
 
-0.99248916310.5%
 
ValueCountFrequency (%) 
0.2428309472126.0%
 
0.1238273901136.5%
 
0.1093468688126.0%
 
-0.007813712853136.5%
 
-0.157358005810.5%
 

TO_20d_min
Real number (ℝ)

MISSING

Distinct196
Distinct (%)99.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.2836789397
Minimum-0.8630996896
Maximum2.603854558
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:57.362254image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8630996896
5-th percentile-0.800821967
Q1-0.6130921575
median-0.02417623686
Q30.8719919972
95-th percentile2.193930371
Maximum2.603854558
Range3.466954248
Interquartile range (IQR)1.485084155

Descriptive statistics

Standard deviation0.9820311789
Coefficient of variation (CV)3.461769774
Kurtosis-0.6213726183
Mean0.2836789397
Median Absolute Deviation (MAD)0.6494842912
Skewness0.7346964737
Sum56.16843006
Variance0.9643852363
MonotocityNot monotonic
2020-12-18T22:50:57.548960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2.3787690621.0%
 
1.93185107621.0%
 
0.502114835610.5%
 
-0.53008277310.5%
 
0.364346032710.5%
 
1.28941353310.5%
 
1.56084469810.5%
 
0.874124479810.5%
 
1.29973283810.5%
 
-0.0800811588510.5%
 
Other values (186)18693.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.863099689610.5%
 
-0.859012793110.5%
 
-0.842643491510.5%
 
-0.830048380110.5%
 
-0.823253534610.5%
 
ValueCountFrequency (%) 
2.60385455810.5%
 
2.53748049310.5%
 
2.44851557510.5%
 
2.43979237410.5%
 
2.3787690621.0%
 

turnover_vol_5d_min
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct194
Distinct (%)98.0%
Missing2
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean0.118795039
Minimum-0.8944611524
Maximum3.614485608
Zeros0
Zeros (%)0.0%
Memory size1.6 KiB
2020-12-18T22:50:57.722722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8944611524
5-th percentile-0.7902017633
Q1-0.5763043958
median-0.1658641626
Q30.4046977117
95-th percentile2.223313856
Maximum3.614485608
Range4.50894676
Interquartile range (IQR)0.9810021076

Descriptive statistics

Standard deviation0.9851021978
Coefficient of variation (CV)8.292452329
Kurtosis3.309890964
Mean0.118795039
Median Absolute Deviation (MAD)0.4562191389
Skewness1.806551218
Sum23.52141772
Variance0.9704263401
MonotocityNot monotonic
2020-12-18T22:50:57.932232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.61448560852.5%
 
0.113428201610.5%
 
-0.521212726410.5%
 
-0.630917345610.5%
 
-0.0729945370310.5%
 
-0.816445993510.5%
 
-0.834016966910.5%
 
1.52786636310.5%
 
0.1651478310.5%
 
2.5192098210.5%
 
Other values (184)18492.0%
 
(Missing)21.0%
 
ValueCountFrequency (%) 
-0.894461152410.5%
 
-0.889069345810.5%
 
-0.85266551510.5%
 
-0.834016966910.5%
 
-0.824624849110.5%
 
ValueCountFrequency (%) 
3.61448560852.5%
 
3.35500785810.5%
 
2.96757161610.5%
 
2.58242765710.5%
 
2.5192098210.5%
 

Interactions

2020-12-18T22:49:46.811852image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:46.937599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:47.072911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:47.192321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:47.315725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:47.434094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:47.556549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:47.677246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:47.798194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:47.928532image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:48.080129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:48.217453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:48.343895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:48.477182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:48.609231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:48.733100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:48.849948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:48.971773image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:49.101044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:49.251343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:49:49.386252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-12-18T22:50:15.269923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:15.402340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:15.535989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:15.664858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:15.791382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:15.921377image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:16.064538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-12-18T22:50:16.315574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:16.452044image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:16.585190image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:16.717663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:16.853529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:16.974847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:17.128619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:17.255892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:17.389332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:17.515306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:17.656067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:17.784808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:18.116330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:18.393321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:18.593224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:18.863661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:19.007287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:19.160379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:19.304234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:19.444226image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:19.792945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:20.100393image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:20.485435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:20.631392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:20.781546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:21.118443image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:21.450983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:21.698006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-12-18T22:50:22.301293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:22.541903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:22.739719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:22.886987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:23.016935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:23.157693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:23.293105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:23.567049image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:23.843024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:24.145483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:24.448313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:24.600891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:24.735869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:24.864462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:24.994379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:25.132095image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:25.374718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:25.550977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:25.689559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:25.820286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:25.951316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:26.083003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:26.224261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:26.358162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:26.489630image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:26.622598image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:26.753231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:26.884204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:27.015074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:27.161283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:27.291846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:27.426458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:27.550593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:27.688789image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-12-18T22:50:27.949952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:28.074272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-12-18T22:50:28.345751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:28.467107image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:28.587388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:28.719605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:28.846579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-12-18T22:50:29.891715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:30.018695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-12-18T22:50:30.280993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:30.408540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:30.548166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-12-18T22:50:30.827567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:30.951185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:31.088376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:31.344637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:31.507483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:33.824876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:34.040428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:34.185948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:34.321032image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:34.520256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:34.991288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:35.184656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:35.324936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:35.563571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:35.698405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:35.846503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:35.975347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:36.165079image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-12-18T22:50:36.528606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:36.664007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:36.821626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:37.039013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:37.312419image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:37.553660image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:37.738496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:37.906723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:38.051391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:38.206870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:38.407421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:38.546408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:38.696299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:38.835417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:38.966163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:39.137272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:39.398275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:39.538820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:39.670413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:39.803548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:39.931451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:40.076116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:40.226747image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:40.358450image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:40.502192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:40.739727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:40.882542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:41.020371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:41.163043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:41.296539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:41.506697image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:41.736074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:41.873988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:42.025142image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:42.301121image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:42.606042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:42.832278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:43.018794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:43.164922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:43.309077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:43.448162image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:43.627822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:43.909290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:44.057173image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:44.276245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:44.411855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:44.539936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:44.694216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:44.822439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:44.999146image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:45.369624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:45.603424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:45.916668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:46.059505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:46.215920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:46.371774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:46.702631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:46.956382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:47.542360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:48.048350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-12-18T22:50:58.427124image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-18T22:50:59.016733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-18T22:50:59.383906image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-18T22:51:00.096244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-18T22:50:48.336743image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:48.643631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:48.994749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-12-18T22:50:49.513687image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

dateTO_100d_minBP_LF_minn_cashflow_actTO_5d_minDAVOL20_minturnover_vol_5d_maxDAVOL5_minNCFP_minVSTD_6d_minopincome_of_ebtturnover_vol_100d_minocf_to_orcurrent_ratiowgt_turn_6mVSTD_6d_maxPPReversal_20_minturnover_vol_20d_maxassets_turnTO_20d_minturnover_vol_5d_min
02013-10-060.1682890.4542330.8589190.244263-0.1950560.4578840.3447051.2278661.196680-0.064393-0.2919960.736647-0.441261-0.0679790.0510360.689746-0.354529-0.157358-0.1761882.216929
12013-10-130.1787770.4666421.0986010.110674-0.2129290.4140890.1360021.223516-0.160803-0.351811-0.3225270.562331-0.476521-0.0679790.0205060.643220-0.3229180.242831-0.1808960.094927
22013-10-200.2070230.5058881.098601-0.295072-0.264660-0.393954-0.4061051.244664-0.368445-0.351811-0.3461840.562331-0.476521-0.067979-0.4894140.505721-0.2884170.242831-0.163815-0.211482
32013-10-270.0568660.5930301.098601-0.390718-0.344878-0.623192-0.5377211.291622-0.520124-0.351811-0.4980510.562331-0.476521-0.067979-0.6633270.396481-0.2831360.242831-0.197837-0.419145
42013-11-03-0.0056920.9746991.098601-0.641950-0.640683-0.632117-0.8451371.497289-0.621366-0.351811-0.4902620.562331-0.476521-0.067979-0.6685240.130822-0.2480430.242831-0.395899-0.528168
52013-11-10-0.0873551.0040101.098601-0.747331-0.958977-0.670304-0.9712301.513084-0.764025-0.351811-0.4784860.562331-0.476521-0.067979-0.714226-0.246287-0.5157030.242831-0.614490-0.762320
62013-11-17-0.1448221.1732201.098601-0.781376-1.115107-0.732497-1.0010591.604264-0.687390-0.351811-0.4598070.562331-0.476521-0.067979-0.753946-0.600759-0.7432160.242831-0.727739-0.623085
72013-11-24-0.2081530.9674381.098601-0.717976-1.155653-0.761073-0.9075141.493376-0.692775-0.351811-0.4486800.562331-0.476521-0.067979-0.764006-0.748109-0.7965770.242831-0.794200-0.632697
82013-12-01-0.2837640.9966421.098601-0.660600-1.121781-0.760532-0.7687251.509113-0.723013-0.351811-0.4667550.562331-0.476521-0.067979-0.762483-0.748365-0.8536690.242831-0.799913-0.708266
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Last rows

dateTO_100d_minBP_LF_minn_cashflow_actTO_5d_minDAVOL20_minturnover_vol_5d_maxDAVOL5_minNCFP_minVSTD_6d_minopincome_of_ebtturnover_vol_100d_minocf_to_orcurrent_ratiowgt_turn_6mVSTD_6d_maxPPReversal_20_minturnover_vol_20d_maxassets_turnTO_20d_minturnover_vol_5d_min
1902017-05-28-0.9214300.0359990.556155-0.838501-0.494155-0.471768-0.5858580.313519-0.783988-0.680706-0.8283060.996413-0.692588-0.891652-0.396413-1.084694-0.759860-0.696955-0.813412-0.889069
1912017-06-04-0.908803-0.0183610.556155-0.525678-0.265614-0.4425030.2232910.296261-0.052180-0.680706-0.8208620.996413-0.692588-0.891652-0.360318-0.867406-0.736527-0.696955-0.751755-0.129429
1922017-06-11-0.914880-0.0639350.556155-0.642137-0.281652-0.6013020.0359700.281792-0.248809-0.680706-0.8232180.996413-0.692588-0.891652-0.520301-0.766292-0.734977-0.696955-0.783900-0.340146
1932017-06-18-0.952374-0.0219160.556155-0.838864-0.231187-0.632121-0.4666290.295132-0.790437-0.680706-0.8433020.996413-0.692588-0.891652-0.544729-0.531059-0.733937-0.696955-0.795377-0.894461
1942017-06-25-0.944900-0.1214890.556155-0.637362-0.085975-0.3143230.0932210.2635210.303821-0.680706-0.8326840.996413-0.692588-0.891652-0.212443-0.292960-0.646792-0.696955-0.7483650.186466
1952017-07-02-0.941140-0.1014330.556155-0.801341-0.228413-0.778024-0.3637880.269888-0.683952-0.680706-0.8334890.996413-0.692588-0.891652-0.6901060.006138-0.700936-0.696955-0.793047-0.786609
1962017-07-09-0.936753-0.0744851.041811-0.789564-0.210457-0.648932-0.3321050.640778-0.386691-0.596680-0.8348810.924636-0.756547-0.891652-0.5518410.187441-0.704476-0.422746-0.789245-0.500418
1972017-07-16-0.941858-0.0542671.041811-0.757915-0.175869-0.701999-0.1787110.649679-0.652882-0.596680-0.8332550.924636-0.756547-0.891652-0.6089100.335051-0.715600-0.422746-0.784917-0.754597
1982017-07-23-0.9525560.1029051.041811-0.733017-0.272862-0.707694-0.0969740.718870-0.379001-0.596680-0.8336010.924636-0.756547-0.891652-0.6397010.296055-0.836321-0.422746-0.830048-0.442337
1992017-07-30-0.9557020.0584151.041811-0.808279-0.317206-0.856273-0.3246370.699284-0.700613-0.596680-0.8321920.924636-0.756547-0.891652-0.7740330.179987-0.847037-0.422746-0.842643-0.804742